1.
Abstract:
Drinking water quality plays an important role in public health, meriting its protection. In analyzing nine districts in Vilnius Municipality for relationships between water quality and health, rates of gastrointestinal disease were found to be 34%-151% higher in districts with higher turbidity concentrations and rates of congenital anomalies 59% higher in districts with higher lead concentrations. While this was a preliminary study, it will hopefully demonstrate the need to make the protection drinking water quality a priority for Vilnius city.
2. Introduction:
The legacy of the Soviet Union, a nascent economy, and recently restructured environmental and health protection regime pose a great challenge to drinking water quality in the Republic of Lithuania (Feshbach 1995) (Kadunas 1997). In the capital city of Vilnius, 19 well fields supply approximately 155,000 cubic meters per day to the residents, comprising the bulk of drinking water consumed (Klimas 1998). These 19 well fields feed nine distinct municipal water supply districts as shown in Map 1. Many of these well fields are located in urbanized areas where the threat of anthropogenic contamination is high, raising questions about water quality and the influence on public health. Additionally, it is known that turbidity can compromise disinfection and serve as a medium for microbiological growth, which can lead to gastrointestinal illness (Schwartz 1997). The influence of nickel and lead on kidney disorders and birth defects is also documented in epidemiological work (ATSDR. 1993; ATSDR. 1996). In this study, I examined public health data from years 1991 to 1995 in conjunction with sets of water quality data from these districts to see if perhaps any relationships exist in Vilnius. Of interest is the indirect influence that turbidity might have on rates of intestinal infectious diseases (International Classification of Diseases-9th Revision (ICD-9) codes 001-009), gastritis and duodenitis (535), disorders of stomach function (536), and diseases of the esophagus, stomach, and duodenum (530-537). Also of interest is the influence of nickel levels on kidney infections (590), and the influence of lead on congenital anomalies (740-759).
3. Hypotheses:
HO(1): Water supply districts with high turbidity as measured at the tap do not have higher instances of ICD-9 diseases 001-009 (grouped), 530-537 (grouped), 535, and 536. (Each hypothesis was tested individually).
HA(1): Water supply districts with high turbidity as measured at the tap do have higher instances of ICD-9 diseases 001-009 (grouped), 530-537 (grouped), 535, and 536.
HO(2): Water supply districts with high turbidity as measured at the treatment facility, i.e. the source, do not have higher instances of ICD-9 diseases 001-009 (grouped), 530-537 (grouped), 535, and 536. (Each hypothesis was tested individually).
HA(2): Water supply districts with high turbidity as measured at the treatment facility do have higher instances of ICD-9 diseases 001-009 (grouped), 530-537 (grouped), 535, and 536.
HO(3): Water supply districts with higher nickel concentrations as measured at the treatment facility do not have higher instances of ICD-9 590.
HA(3): Water supply districts with higher nickel concentrations at the treatment facility do have higher instances of ICD-9 590.
HO(4): Water supply districts with higher lead concentrations as measured at the treatment facility do not have higher instances of ICD-9 740-759 (grouped).
HA(4): Water supply districts with higher lead concentrations as measured at the treatment facility do have higher instances of ICD-9 740-759 (grouped).
HO(5): Vilnius Municipality complies with Lithuanian Hygienic Norm 24:1998 for levels of turbidity, nickel, and lead in drinking water.
HA(5): Vilnius Municipality does not comply with Lithuanian Hygienic Norm 24:1998 for levels of turbidity, nickel, and lead in drinking water.
4.
Data, Methods, & Analysis:
Public health data were obtained from the database maintained by the
Vilnius Public Health Center or Vilniaus Visuomenes Sveikatos Centras
(VVSC). Each time a patient up to age
19 visits a polyclinic in Vilnius, a record is sent to the VVSC recording the
diagnosis, the patient’s age, residence, the date, and other useful
information. The data spanned the years
1991 to 1995 for ages 0-19 and comprised over 1 million records, classified by
the International Classification of Diseases, 9th Revision (ICD-9) codes. Each street in the public health database
was assigned to the water supply district in which it is located. This allowed
for the creation of queries based on the water supply systems. Utilizing a data table that also included
the population living on each street in Vilnius, monthly disease rates were
calculated per 100,000 residents. Water
quality data were obtained from the local water company, Vilniaus Vandenys, for
nickel, lead, and turbidity concentrations as measured at the nine separate
water supply district reservoirs. Water
quality data also came from the VVSC that performs turbidity testing in the
municipal water supply system at the tap.
The first step in analysis involved the
coding of each district as high or low based on median values of turbidity at
the tap, turbidity at the treatment facility, nickel, and lead. I arbitrarily selected these threshold
values for turbidity as measured at the tap at 1.4 mg/L, turbidity measured at
the treatment facility at 0.75 mg/L, nickel at 0.018 mg/L, and lead at 0.0165
mg/L. Coding the data allowed me to overcome the problems I had with very
non-normal distributions of water quality measurements and a very large amount
of variability. The coding is shown in Table 1. Descriptive statistics for the
water quality data and public health data can be seen in Table 2 and Table 3,
respectively.
Second, based on districts grouped high and low for each water quality indicator, an ANOVA Test was performed to determine if there was a significant difference between the disease rates of the high group and disease rates of the low group. Additionally, a Wilcoxon/Kruskal-Wallis Rank-Sum Test was performed to test for differences between the high and low groups. This test was chosen given that the public health data are not normally distributed based on the Shapiro-Wilk Test and also possess significantly unequal variances. The results of these analyses are shown in Table 4.
Lastly, to test for compliance with
Lithuanian water quality standards in Hygienic Norm 24:1998 (HN 24:1998), a
Wilcoxon Signed-Rank Test was performed comparing the means of water quality
samples to the standard. The results
are displayed in Table 5.
5.
Sources of Error, Discussion & Conclusions:
For this analysis, age distribution was assumed to be equal among districts, variances in diagnosis rates were assumed to be reasonably equal, and the distributions of diagnosis rates were assumed to be relatively normal. The confounding effects of socioeconomic status, temperature, and other factors that influence public health were assumed to be negligible (Anderson 1997).
In order to reject the null hypothesis, the districts with high levels of a given water quality indicator had to test significantly higher for a given disease rate at an α-level of 0.05 and a Power level of 0.90 in the ANOVA Test. The Power of the analysis in Table 4 is the probability of not rejecting the null hypothesis when it should have been rejected. These districts also had to test higher and be significant in the Wilcoxon/Kruskal-Wallis Rank-Sum Test at an α-level of 0.05. This was an extra built-in measure of security to hopefully reduce the risk of coming to an incorrect conclusion, given the non-normal nature of the distributions.
Based on these standards, I rejected HO(1) and accepted HA(1)
that districts with high turbidity as measured at the tap do have higher
instances of gastritis and duodenitis (535), disorders of stomach function
(536), and diseases of the esophagus, stomach, and duodenum (530-537). I further rejected HO(2), and accepted HA(2)
that districts with high turbidity as measured at the treatment facility do
have higher instances of ICD-9 diseases
001-009 (grouped), 530-537 (grouped), 535, and 536. The ANOVA test showed a sizable difference in the means of the
high and low group in several instances, for example diseases of the esophagus,
stomach, and duodenum (530-537) where on average 83 diagnoses per 100,000 per
month occurred in the high turbidity districts, versus 47 per month in the
low.
With respect to nickel, I found no significant difference in both the ANOVA test and the Wilcoxon/Kruskal-Wallis Test, thereby not refuting HO(3). For lead, I did find variability among the groups that was more than one would expect to find by chance. Rejecting HO(4), I accepted HA(4) that districts with higher lead concentrations as measured at the treatment facility do have higher instances of congenital anomalies (740-759 grouped).
For H(5),
the Wilcoxon Signed Rank Test did not show at an α-level of 0.05 that the
water measurements did not comply with water standards and HO(5) was not rejected (Ministry
of Health Protection 1998). Although beyond the scope of this study, while
measurements throughout the city did not seem out of compliance with Lithuanian
standards, measurements in individual districts were in some cases
substantially higher and suggest compliance problems.
In summary, districts with higher turbidity concentrations seemed to have 34%-151% higher instances of gastrointestinal disorders and districts with higher lead concentrations seemed to have 59% higher instances of congenital anomalies for the 0-19 year age-group. It is hoped that these preliminary findings will encourage Vilnius Municipality to seek ways to protect drinking water quality and educate its citizens about the influence of water quality on public health.
6.
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